Tracking the Intangible: Quantifying Effort in NFL Running Backs
Image source: The Tower
Introduction
American Football is one of the most-watched and popular sports in the U.S., known for its quick decision-making, complex tactics, and athletically demanding displays of strength, endurance and speed. However, many traditional statistics miss the “how” behind value plays such as yards gained after contact, expected points added, etc. As such, we must ask ourselves: what does it take to gain a yard? Is a player exceeding their capabilities, therefore, making an effort? If so, how often or how close does a player comes to his “best”?
Our motivation for this project centered on these very questions. Our goal is to develop a metric to measure effort. We acknowledge that effort is a complex, intangible, and most often subjective concept, influenced by player position, opposing defense, snap count or play volume to play call/assignment. Effort is also heavily contextual and multi-faceted, often shaped by the emotional and mental state as well as the thinking process of a player. Given the available data, we are going to focus on one facet: the raw physical capabilities of a player — specifically, acceleration and speed.
By quantifying effort, we hope to provide not only to teams and sport data analysts, but also fans with deeper insight into what and how football players are truly contributing on the field — even when it’s not reflected in conventional stats or even the outcome of a game. We also aspire to address or even fill in some of the gaps in the current literature around quantifying effort in American football since most research focus on win probabilities and other outcomes at the game level rather than individualized capabilities.
Data
The data used for this project were the games, plays, players, tracking data sets from NFL Big Data Bowl 2022, weeks 1-9 on Kaggle (NFL Big Data Bowl 2025).
Data pre-porcessing
- We limited our dataset to NFL running backs with more than 20 rushes per play during the 2022 season.
- We limited the rows to running plays where a running back (RB) is the ball carrier.
- We Trimmed each play to frames between handoff and at end of a play.
Methods
Individual Acceleration-Speed Profile
- Our main methodological approach to addressing this question centered around previous research that has explored soccer players reaching their theoretical max capacity in terms of their raw acceleration and speed (Morin et al. 2021). We adopted a similar framework by plotting an individual running back’s frames with with acceleration on the y-axis and speed on the x-axis.
Metric #1: Linear Regression
- Effort = percentage of points above the transformed regression line
Effort v1
\left(\sum\limits_{i=1}^{n_{\text{below}}} {\frac{1}{1+d_i}}\right)\bigg/n_{\text{below}} ➜️ Quantifies how close a player comes to his “best” (99th percentile) accelerations
➜ Saquon Barkley: 0.152
➜ Rex Burkhead: 0.149
Linear quantile regression for acceleration(mph/s) vs speed(mph)
Metric #2: Additive Quantile Regression Model
Effort v2
Percentage of total points that lie in between the percentile P_{99} and P_{99}-3
➜ Quantifies how often a player comes close to his “best” (99th percentile) accelerations
➜ Saquon Barkley: 0.074
➜ Rex Burkhead: 0.069
Top 10
Results
[Describe your results. This can include tables and plots showing your results, as well as text describing how your models worked and the appropriate interpretations of the relevant output. (Note: Don’t just write out the textbook interpretations of all model coefficients. Instead, interpret the output that is relevant for your question of interest that is framed in the introduction)]]
Discussion
Give your conclusions and summarize what you have learned with regards to your question of interest. Are there any limitations with the approaches you used? What do you think are the next steps to follow-up your project?
- AS didn’t translate into football (maybe works for other sports where you don’t get tackled but just run at hand-off)
- there are many dependencies in the nfl
- this is a productive place to start attempting
- further research should look into taking into account
Appendix
Tables
- Percentage of total points that lie in between the percentile P_{99} and P_{99}-3
- This effort metric quantifies how often a player comes close to his “best” (99th percentile) accelerations
Trying a different interactive layout
Acknowledgements
- Used Dr. Ron Yurko and Quang Nguyen’s code to calculate distance from the nearest defender (Nguyen 2023)
- Still using the non-linear quantile regression plot? (Ding 2024)
References
Ding, Peng. 2024. Linear Models and Extensions. Chapman & Hall. https://arxiv.org/pdf/2401.00649.
Morin, Jean-Benoit, Yann Le Mat, Cristian Osgnach, Andrea Barnabò, Alessandro Pilati, Pierre Samozino, and Pietro E. di Prampero. 2021. “Individual Acceleration-Speed Profile in-Situ: A Proof of Concept in Professional Football Players.” Journal of Biomechanics 123: 110524. https://doi.org/https://doi.org/10.1016/j.jbiomech.2021.110524.
NFL Big Data Bowl. 2025. “NFL Big Data Bowl 2025 Dataset.” Kaggle. https://www.kaggle.com/competitions/nfl-big-data-bowl-2022/data.
Nguyen, Quang. 2023. “Turn-Angle.” https://github.com/qntkhvn/turn-angle/blob/main/scripts/01a_prep_rusher_data.R.